Is it possible to inventory a 1,000 km bike network in just over a week? With RoadAI, it is.
Montréal is a great place to bike, thanks to its excellent network of bike lanes that spans over 1,000 km (625 mi)—one of the largest in North America. The Express Bike Network (EBN), also known as the Réseau express vélo (REV), is the backbone of Montréal’s cycling infrastructure, designed for year-round accessibility. Of the network, 234 km consist of bike paths protected by concrete barriers and poles, while 495 km are marked bike lanes with ground markings and signage.
While the network is popular among users, the city had no tool in place to monitor its conditions—an important need, given the significant variation across the network, which makes maintenance planning a complex task.
To better understand the conditions of the paths, the City deployed RoadAI, an AI-powered automated road asset inventory tool that has been highly successful for road network management organizations globally. Although the tool is originally designed to survey driveways, the City of Montréal successfully adapted the (which can be used on a regular smartphone) to survey the entire the bike paths across the EBN in one go. This was made possible thanks to Ariane Garon, a Master’s student in Geography and Geomatics at the University of Montréal. For over a week, Ariane became "the eyes of the city," collecting road condition data across the EBN on her electric bike using RoadAI.
Below, Nam Nguyen, Ing., Ph:D. Engineer Department of Strategic Asset Management of the City of Montreal, shares his experience deploying and using RoadAI.
Smooth integration & comprehensive support
According to Nguyen, the solution exceeded his expectations. From deployment to the compilation of results, RoadAI seamlessly met the city's needs and integrated smoothly into existing methodologies. Comprehensive technical support throughout each stage of the project ensured a smooth and efficient deployment.
"While surveying the network, I was pleasantly surprised by the ease of use of the app on an Android phone. Data management and transfer, once collected in the field, happen automatically in the background, saving significant time and reducing the potential for errors," says Nguyen.
Powerful features
In processing mode, Nguyen particularly appreciates the platform's rich functionality. For instance, users can create a customized interactive map that incorporates several features: survey data management, thematic maps in both survey mode and compiled mode, all tailored to the client's repository.
“We are pleasantly surprised by how easy it is to use the RoadAI app. In processing mode, we appreciate the convergent results obtained with AI and the enriching, well-engineered features”
Nam Nguyen, Ing., Ph:D., Engineer, Department of Strategic Asset Management, the City of Montreal
As a cloud-based solution, RoadAI leverages AI to automatically process and identify environmental deficiencies, delivering results consistently within 48 hours. Nguyen expresses his appreciation for the development team, highlighting their ability to integrate robust GIS methodologies, especially the processing and correction of GPS data, and the successful integration of the city’s repository into the platform.
Effective reporting functionality
In report mode, the City of Montreal utilizes two types of reports: a detailed report with a resolution up to 5 meters, and a section assessment, both of which align perfectly with the city’s needs. Furthermore, users can continuously track not only the quantity of identified deficiencies but also their precise locations in each analyzed image. "We find that the modules for detecting potholes, road markings, and street furniture offer highly valuable insights," adds Nguyen.
Next in the project, the city will validate the results obtained from RoadAI compared to traditional methods (LCMS monitoring vehicle); after that, performance indicators for cycle lanes with be developed.
